Deep Reinforcement Learning for Automated Cyber Threat Hunting
A Next-Gen Solution
Keywords:
Deep Reinforcement Learning, Cyber Threat Hunting, Anomaly Detection, Decision-Making, Threat MitigationAbstract
In the face of increasingly sophisticated cyber threats, organizations are compelled to adopt advanced methodologies for cyber threat hunting. This paper proposes the utilization of Deep Reinforcement Learning (DRL) to automate the process of cyber threat hunting, focusing on real-time anomaly detection, effective decision-making, and efficient threat mitigation within enterprise systems. The integration of DRL into cybersecurity operations represents a paradigm shift from traditional approaches that often rely on static rule-based systems or signature-based detection methods. The proposed framework harnesses the adaptive learning capabilities of DRL algorithms, allowing for continuous improvement in threat detection and response strategies. By simulating various cyber threat scenarios, this research explores the effectiveness of DRL in identifying anomalies and initiating preemptive measures against potential attacks. The findings indicate that a DRL-based approach not only enhances the accuracy of threat detection but also significantly reduces response times, thereby improving overall cybersecurity posture. The implications of this research extend to various sectors, emphasizing the need for organizations to adopt automated solutions to remain resilient against evolving cyber threats.
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